Abstract
As multilingual text increases, the analysis of multilingual data plays a crucial role in statistical translation models, cross-language information retrieval, the construction of parallel corpus, bilingual information extraction and other fields. In this paper, we introduce convolutional neural network and propose auto-associative memory for the fusion of multilingual data to classify multilingual short text. First, the open-source tool word2vec is used to extract word vector for textual representation. Then, the auto-associative memory relationship can extract the multilingual document semantic, which need to calculate the statistical relevance of word vector between different languages. A critical problem is the domain adaptation of classifiers in different languages and we solve it by transforming multilingual text features. In order to fuse a dense combination of high-level features in multilingual text semantics, we introduce convolutional neural network into the model, and output classification prediction results. This model can process multilingual textual data well. Experiments show that convolutional neural network combined with auto-associative memory improves classification accuracy by 2 to 6% in multilingual text classification, compared to other classic models. Furthermore, the proposed model reduces the dependence of multilingual text on the parallel corpus, thus have good expansibility for multilingual data.
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References
Faruqui, M., Dyer, C.: Improving vector space word representations using multilingual correlation. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp. 462–471 (2014)
Gliozzo, A., Strapparava, C.: Exploiting comparable corpora and bilingual dictionaries for cross-language text categorization. In: Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics, pp. 553–560. Association for Computational Linguistics (2006)
Guo, J., Che, W., Yarowsky, D., Wang, H., Liu, T.: Cross-lingual dependency parsing based on distributed representations. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 1234–1244 (2015)
Hanneman, G., Lavie, A.: Automatic category label coarsening for syntax-based machine translation. In: Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 98–106. Association for Computational Linguistics (2011)
Harris, Z.S.: Mathematical structures of language. In: Tracts in Pure and Applied Mathematics (1968)
He, W.: Research on wordNet based Chinese English cross-language text similarity measurement. Master’s thesis, Shanghai Jiao Tong University (2011)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Liu, J., Cui, R.Y., Zhao, Y.H.: Cross-lingual similar documents retrieval based on co-occurrence projection. In: Proceedings of the 6th International Conference on Computer Science and Network Technology, pp. 11–15 (2017)
Luo, Y., Wang, M., Le, Z., Lu, X.: Bilingual latent semantic corresponding analysis and its application to cross-lingual text categorization. J. China Soc. Sci. Tech. Inf. 32(1), 86–96 (2013)
Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 (2013)
Peng, Z.: Research of cross-language text correlation detection technology. Master’s thesis, Central South University (2014)
Potthast, M., Stein, B., Anderka, M.: A Wikipedia-based multilingual retrieval model. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 522–530. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78646-7_51
Sorg, P., Cimiano, P.: An experimental comparison of explicit semantic analysis implementations for cross-language retrieval. In: Horacek, H., Métais, E., Muñoz, R., Wolska, M. (eds.) NLDB 2009. LNCS, vol. 5723, pp. 36–48. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12550-8_4
Tang, G., Xia, Y., Zhang, M., Zheng, T.: Cross-lingual document clustering based on similarity space model. J. Chin. Inf. Process. 26(2), 116–120 (2012)
Tang, M., Zhu, L., Zou, X.C.: Document vector representation based on word2vec. Comput. Sci. 43(6), 214–217 (2016)
Vulić, I., Moens, M.F.: Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363–372. ACM (2015)
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This research was financially supported by State Language Commission of China under Grant No. YB135-76. We would like to thank editor and referee for their careful reading and valuable comments.
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Liu, J., Cui, R., Zhao, Y. (2018). Multilingual Short Text Classification via Convolutional Neural Network. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_3
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DOI: https://doi.org/10.1007/978-3-030-02934-0_3
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